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Integrative approaches for analysis of mRNA and microRNA high-throughput data
- Source :
- Computational and Structural Biotechnology Journal, Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 1154-1162 (2021)
- Publication Year :
- 2021
- Publisher :
- Research Network of Computational and Structural Biotechnology, 2021.
-
Abstract
- Highlights • Review on tools and databases linking miRNA and its mRNA targetome. • Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects. • Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions.<br />Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.
- Subjects :
- CNN, convolutional neural network
RNASeq, high-throughput RNA sequencing
In silico
Biophysics
circRNA, circular RNA
CLASH, cross-linking, ligation and sequencing of hybrids
Computational biology
Review Article
Biology
computer.software_genre
Biochemistry
TDMD, target RNA-directed miRNA degradation
Transcriptome
lncRNA, long non-coding RNA
03 medical and health sciences
0302 clinical medicine
Structural Biology
Target prediction
microRNA
miRNA, microRNA
Genetics
Transcriptomics
Throughput (business)
Gene
TF, transcription factors
030304 developmental biology
GO, gene ontology
0303 health sciences
Messenger RNA
PCA, principal component analysis
Matrix factorization
RNA
CDS, coding sequence
CCA, canonical correlation analysis
ICA, independent component analysis
mRNA, messenger RNA
Computer Science Applications
NGS, next-generation sequencing
NMF, non-negative matrix factorization
030220 oncology & carcinogenesis
CLIP, cross-linking immunoprecipitation
Data integration
computer
TP248.13-248.65
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 20010370
- Volume :
- 19
- Database :
- OpenAIRE
- Journal :
- Computational and Structural Biotechnology Journal
- Accession number :
- edsair.doi.dedup.....f21663e07a220d318e6ba9e02270e42c